Resilient control under denial-of-service and uncertainty: An adaptive dynamic programming approach

TL;DR

This paper introduces a novel adaptive dynamic programming framework that enables continuous-time linear systems to maintain stability and optimal control despite denial-of-service attacks and uncertainties, by learning controllers from real-time data.

Contribution

It combines reinforcement learning and output regulation theory to develop resilient controllers that adaptively learn from real-time data under attack conditions.

Findings

Controllers maintain stability under DoS attack durations.

The framework effectively learns optimal control policies from real-time data.

Simulation results confirm robustness against attacks and uncertainties.

Abstract

In this paper, a new framework for the resilient control of continuous-time linear systems under denial-of-service (DoS) attacks and system uncertainty is presented. Integrating techniques from reinforcement learning and output regulation theory, it is shown that resilient optimal controllers can be learned directly from real-time state and input data collected from the systems subjected to attacks. Sufficient conditions are given under which the closed-loop system remains stable given any upper bound of DoS attack duration. Simulation results are used to demonstrate the efficacy of the proposed learning-based framework for resilient control under DoS attacks and model uncertainty.